As computer networking technology continues to develop, e-commerce websites are setting up their own search engine to provide product search services to help users to look for products and quickly find products of interest. Product search methods are similar to search methods used by ordinary search engines (such as Baidu, Google, and Bing), but the product search methods have their own characteristics. In typical searches, correlation with query words is considered when ranking search results. However, product searches obtain search results through comprehensive rankings that include a plurality of dimensions, such as historical buyer evaluations of products, reputations of the sellers that publish product information, difficulty or ease with which fraud can be committed, correlation of categories, product prices, as well as user personal preference data on various product objects.
Conventionally, search results typically are displayed in the form of separate pages or cascades, with a predefined quantity of products being displayed on one page or one screen, for example, 40 products on each page. Subsequently, the user can choose to switch to the next page or screen for browsing. With the page-by-page display form, whenever the user turns the page, the user sends another request to the search engine by clicking the corresponding page number or the tab to switch to the next page. The user then browses products on other pages. With the cascade display form, another request is sent to the search engine when the cursor or slider is dragged down, and more web page content is displayed in place of the former content.
Because the search results at the present time are single-output, natural search results, no re-ranking occurs when switching to search results on a different page. A display sequence of the search results is not related to user click or browsing actions. For example, if a user enters the query word “Nike” when conducting a product search and the user clicks on 10 products on the first page of the corresponding search results, when the user switches to the second page of the search results, the search results displayed on the second page are unrelated to the search results that were or were not clicked on the first page of the search results. In other words, no dynamic ranking occurs based on the user actions.
In ordinary search engines, results of the first click action refer to natural search results from query words to be used as a target web page. The natural search results are then adjusted from low to high based on the distance from the target web page (in other words, based on a similarity between total web pages and the target web page). This clarifies the intention of the user's query instead of having multiple terms for a single meaning and multiple meanings for a single term.
Similarity distance calculations between ordinary search pages are not necessarily suitable for product searches because the information (such as title, price, and picture information) displayed on page listings of the natural results of the product searches is compounded with target page product descriptions, evaluation information, retail shop information, transaction records, sales promotion information, attribute information, and various other kinds of information. For example, an “ordinary” search page is a comparison of the similarities between pages. A comparison of product similarities relate to the content of the product itself, and similarities of product information between pages are not important. Because a product information page includes user's feedback of the product, other information recommended by the product seller, etc., similar products have different content in these areas of product information pages. Even if two products are similar, the product information pages can be very different. Thus, “ordinary” search pages comparisons are not suitable. In addition, the target page information is already unable to represent information on user-clicked natural search results. The target page information may include product feedback from the users, seller information, seller's recommendation information, seller's description of the products, etc. Different sellers selling the same product can have different descriptions, and different users' feedback for products from different sellers can be different. Differences in product recommendation information can exist. Thus, all of the information is difficult to use for determining similarities in products. As such, the target page information does not necessarily reflect information about the product itself, and would be difficult to reflect the user's intent when clicked. Therefore, the similarity between the target web page and the web pages of objects cannot truly represent the similarity to product search results. In addition, dynamic rankings of ordinary search results typically consist of optimizing natural results from query words. The initial search results are used to probe the intentions of the query words. The initial search results may differ considerably from the user's actual intentions. Accordingly, accuracy and user browsing-to-transaction conversion rates of the initial search results are relatively low.